dplR (version 1.4.9)

corr.rwl.seg: Compute Correlations between Series

Description

Computes the correlation between each tree-ring series in a rwl object.

Usage

corr.rwl.seg(rwl,seg.length=50,bin.floor=100,n=NULL, prewhiten = TRUE,
  pcrit=0.05, biweight=TRUE, make.plot = TRUE, label.cex=1,
  floor.plus1 = FALSE, ...)

Arguments

rwl

a data.frame with series as columns and years as rows such as that produced by read.rwl.

seg.length

an even integer giving length of segments in years (e.g., 20, 50, 100 years).

bin.floor

a non-negative integer giving the base for locating the first segment (e.g.,1600, 1700, 1800 AD). Typically 0, 10, 50, 100, etc.

n

NULL or an integer giving the filter length for the hanning filter used for removal of low frequency variation.

prewhiten

logical flag. If TRUE each series is whitened using ar.

pcrit

a number between 0 and 1 giving the critical value for the correlation test.

biweight

logical flag. If TRUE then a robust mean is calculated using tbrm.

make.plot

logical flag indicating whether to make a plot.

label.cex

numeric scalar for the series labels on the plot. Passed to axis.cex in axis.

floor.plus1

logical flag. If TRUE, one year is added to the base location of the first segment (e.g. 1601, 1701, 1801 AD).

...

other arguments passed to plot.

Value

• A list containing matrices spearman.rho, p.val, overall, bins, vector avg.seg.rho. An additional character flags is also returned if any segments fall below the critical value. Matrix spearman.rho contains the correlations each series by bin. Matrix p.val contains the p-values on the correlation for each series by bin. Matrix overall contains the average correlation and p-value for each series. Matrix bins contains the years encapsulated by each bin. The vector avg.seg.rho contains the average correlation for each bin.

Details

This function calculates correlation serially between each tree-ring series and a master chronology built from all series in a rwl object. Correlations are done for each segment of the series where segments are lagged by half the segment length (e.g., 100-year segments would be overlapped by 50-years). The first segment is placed according to bin.floor. The minimum bin year is calculated as ceiling(min.yr/bin.floor)*bin.floor where min.yr is the first year in the rwl object. For example if the first year is 626 and bin.floor is 100 then the first bin would start in 700. If bin.floor is 10 then the first bin would start in 630. Correlations are calculated for the first segment, then the second segment and so on. Correlations are only calculated for segments with complete overlap with the master chronology. For now, correlations are Spearman's rho calculated via cor.test using method="spearman." Each series (including those in the rwl object) is optionally detrended as the residuals from a hanning filter with weight n. The filter is not applied if n is NULL. Detrending can also be done via prewhitening where the residuals of an ar model are added to each series mean. This is the default. The master chronology is computed as the mean of rwl object using tbrm if biweight=TRUE and rowMeans if not. Note that detrending can change the length of the series. E.g., a hanning filter will shorten the series on either end by floor(n/2). The prewhitening default will change the series length based on the ar model fit. The effects of detrending can be seen with series.rwl.plot. The function is typically invoked to produce a plot where each segment for each series is colored by its correlation to the master chronology. Green segments are those that do not overlap completely with the width of the bin. Blue segments are those that correlate above the user-specified critical value. Red segments are those that correlate below the user-specified critical value and might indicate a dating problem.

See Also

corr.series.seg skel.plot series.rwl.plot ccf.series.rwl

Examples

data(co021)
  corr.rwl.seg(co021,seg.length=100,label.cex=1.25)